Beyond the ROC Curve: Activity Monitoring to Evaluate Deep Learning Models in Clinical Settings
We evaluated ‘VITALCARE-SEPS’, a deep learning model for sepsis prediction, using the activity monitoring operator characteristics curve with two different scoring algorithms. This evaluation is crucial as the AMOC curve addresses the time-dependent nature of predictions, providing a more nuanced p...
Saved in:
Main Authors: | Hyunwoo CHOO, Kyung Hyun LEE, Sungsoo HONG, Sungjun HONG, Ki-Byung LEE, Chang Youl LEE |
---|---|
Format: | Article |
Language: | English |
Published: |
Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2024-11-01
|
Series: | Applied Medical Informatics |
Subjects: | |
Online Access: | https://ami.info.umfcluj.ro/index.php/AMI/article/view/1074 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Functional Prediction of Hypothetical Proteins from and Validation of the Predicted Models by Using ROC Curve Analysis
by: Md. Amran Gazi, et al.
Published: (2018-12-01) -
Is it Beneficial to Use Different Thresholds Over Time for Early Prediction Model?
by: Sungsoo HONG, et al.
Published: (2024-11-01) -
Relationship between In-Hospital Sepsis Prediction Score and Prevalence of Community-Onset Sepsis: Triage for Sepsis Risk Management
by: Kyung Hyun LEE, et al.
Published: (2024-11-01) -
Comparative analysis of inflammatory biomarkers for the diagnosis of neonatal sepsis: IL-6, IL-8, SAA, CRP, and PCT
by: Chen Ying, et al.
Published: (2025-01-01) -
Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance
by: Pratyush Kumar MAHARANA, et al.
Published: (2024-12-01)